1. Field of the Invention
The invention relates to a machine-implemented process and an electronic device for presenting a multiple-axis, or multiple-axes, graph for a plurality of data sets, in which adjustments are made, to assure proper scaling, to boundaries of multiple reference axes in a manner allowing for optimal comparison between the data sets.
2. Description of the Related Art
It is often desirable to present a plurality of data sets on a single graph. Microsoft Office EXCEL® may be used for such a purpose. The graph of FIG. 1 is presented using Microsoft Office EXCEL®, and shows average educational expenditures for each child in the United States (in thousands of US dollars), average SAT (Scholastic Aptitude Test) scores in the United States, and Government Education Budget Difference(%), which is hypothetical data from 1980 to 1988. Due to the significant difference in scale among the three data sets, however, no meaningful comparison between unsealed data sets is possible, or even likely, using the graph of FIG. 1.
Other current representative graphing tools are available. However, all of these tools are deficient with respect to the manner in which boundaries of the multiple reference axes are selected. For example, assuming that the multiple reference axes are y-axes, if the boundaries of one of the left y-axes are set to be equal to the maximum and minimum values of one of the data sets, and the boundaries of one of the right y-axes are set to be equal to the maximum and minimum values of one of the other data sets, although the fluctuations in the resulting curves for the multiple data sets are clearly visible, completely erroneous conclusions may be drawn from the resulting graph since such an approach of setting the boundaries of the multiple axes is arbitrary. That is, with such an approach, the boundaries are set for the multiple axes without taking into consideration any relation between the multiple data sets, leading to curves that may suggest correlations between the data sets where there are none or lead to correlations which may be inaccurate, or more likely will be.